Concurrent fault localization using ANN

Debolina Ghosh (), Jay Prakash Singh () and Jagannath Singh ()
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Debolina Ghosh: Manipal University Jaipur
Jay Prakash Singh: Manipal University Jaipur
Jagannath Singh: KIIT Deemed to be University

International Journal of System Assurance Engineering and Management, 2023, vol. 14, issue 6, No 24, 2345-2353

Abstract: Abstract The software is becoming more capable of providing better solutions to our day-to-day activities. In order to increase performance, concurrent programs are always preferred. But the concurrency produces obstacles to different phases of software development, including testing and debugging. Finding faults in a concurrent program is always a challenging task due to the presence of many threads overlapping each other, problems in sharing memories, etc. In this paper, we have proposed a Back propagation neural network (BPNN) to generate ranks for each class of a given program. These ranks indicate the probability of a fault being present in each class. The model is trained using test case coverage data, and it is tested using virtual test cases. In all three case studies, the fault localization technique assigned the highest rank to the classes where the actual faults were implanted. By using BPNN and training the model with test case coverage data, the technique shows promising results in identifying the classes where faults are likely to occur. This can greatly aid in the testing and debugging processes of concurrent programs, improving their overall performance.

Keywords: Fault localization; Artificial neural network; Back propagation; Concurrent programs; Concurrent fault localization; Benchmark programs (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s13198-023-02081-1

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